NDLI: Locality-sensitive support vector machine by exploring local correlation and global regularization

Content Provider	IEEE Xplore Digital Library
Author	Guo-Jun Qi Qi Tian Huang, T.
Copyright Year	2011
Description	Author affiliation: Beckman Institute, University of Illinois at Urbana-Champaign (Guo-Jun Qi; Huang, T.) \|\| Department of Computer Science, University of Texas at San Antonio (Qi Tian)
Abstract	Local classifiers have obtained great success in classification task due to its powerful discriminating ability on local regions. However, most of them still have restricted generalization in twofold: (1) each local classifier is sensitive to noise in local regions which leads to overfitting phenomenon in local classifiers; (2) the local classifiers also ignore the local correlation determined by the sample distribution in each local region. To overcome the above two problems, we present a novel locality-sensitive support vector machine (LSSVM) in this paper for image retrieval problem. This classifier applies locality-sensitive hashing (LSH) to divide the whole feature space into a number of local regions, on each of them a local model can be better constructed due to smaller within-class variation on it. To avoid these local models from overfitting into locality-sensitive structures, it imposes a global regularizer across local regions so that local classifiers are smoothly glued together to form a regularized overall classifier. local correlation is modeled to capture the sample distribution that determines the locality structure of each local region, which can increase the discriminating ability of the algorithm. To evaluate the performance, we apply the proposed algorithm into image retrieval task and competitive results are obtained on the real-world web image data set.
Starting Page	841
Ending Page	848
File Size	1560146
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995378
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Training Correlation Support vector machines Matrices Feature extraction Semantics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Motion Part Regularization: Improving action recognition via trajectory group selection

Function Dot Product Kernels for Support Vector Machine

Activity recognition using dynamic subspace angles

Support Vector Machines in face recognition with occlusions

On-line Boosting and Vision

Feature seeding for action recognition

A Local Region-based Approach to Gender Classi.cation From Face Images

Spectral learning of latent semantics for action recognition

Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

Locality-sensitive support vector machine by exploring local correlation and global regularization

Similar Documents

Motion Part Regularization: Improving action recognition via trajectory group selection

Function Dot Product Kernels for Support Vector Machine

Activity recognition using dynamic subspace angles

Support Vector Machines in face recognition with occlusions

On-line Boosting and Vision

Feature seeding for action recognition

A Local Region-based Approach to Gender Classi.cation From Face Images

Spectral learning of latent semantics for action recognition

Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

Locality-sensitive support vector machine by exploring local correlation and global regularization